Credibility Assessment of the Patient-Specific Modeling of the Aneurysmal Ascending Thoracic Aorta: Verification, Validation and Uncertainty Quantification.

Improving Recognition, Diagnosis, and Management Of Heparin Induced Thrombocytopenia By Implementing a Computer-Based Clinical Decision Support System

BackgroundComputer-based clinical decision support systems (CDSS) are regarded as a key element to enhance decision-making in a healthcare environment to improve the quality of medical care delivery. The concern of having new CDSS unused is still one of the biggest issues in developing countries for the developers and implementers of clinical IT systems. The main objectives of this study are to determine whether (1) the physician’s perceived professional autonomy, (2) involvement in the decision to implement CDSS and (3) the belief that CDSS will improve job performance increase the intention to adopt CDSS. Four hypotheses were formulated and tested.MethodsA questionnaire-based survey conducted between July 2010 and December 2010. The study was conducted in seven public and five private hospitals in Kuala Lumpur, Malaysia. Before contacting the hospitals, necessary permission was obtained from the Ministry of Health, Malaysia and the questionnaire was vetted by the ethics committee of the ministry. Physicians working in 12 hospitals from 10 different specialties participated in the study. The sampling method used was stratified random sampling and the physicians were stratified based on the specialty. A total of 450 physicians were selected using a random number generator. Each of these physicians was given a questionnaire and out of 450 questionnaires, 335 (response rate – 74%) were returned and 309 (69%) were deemed usable.ResultsThe hypotheses were tested using Structural Equation Modeling (SEM). Salient results are: (1) Physicians’ perceived threat to professional autonomy lowers the intention to use CDSS (p < 0.01); (2) Physicians involvement in the planning, design and implementation increases their intention to use CDSS (p < 0.01); (3) Physicians belief that the new CDSS will improve his/her job performance increases their intention to use CDSS (p < 0.01).ConclusionThe proposed model with the three main constructs (physician’s professional characteristic, involvement and belief) explains 47% of the variance in the intention to use CDSS. This is significantly higher than the models addressed so far. The results will have a major impact in implementing CDSS in developing countries.

A taxonomic description of computer-based clinical decision support systems

Many computer software developers and vendors claim that their systems can directly improve clinical decisions. As for other health care interventions, such claims should be based on careful trials that assess their effects on clinical performance and, preferably, patient outcomes. To systematically review controlled clinical trials assessing the effects of computer-based clinical decision support systems (CDSSs) on physician performance and patient outcomes. We updated earlier reviews covering 1974 to 1992 by searching the MEDLINE, EMBASE, INSPEC, SCISEARCH, and the Cochrane Library bibliographic databases from 1992 to March 1998. Reference lists and conference proceedings were reviewed and evaluators of CDSSs were contacted. Studies were included if they involved the use of a CDSS in a clinical setting by a health care practitioner and assessed the effects of the system prospectively with a concurrent control. The validity of each relevant study (scored from 0-10) was evaluated in duplicate. Data on setting, subjects, computer systems, and outcomes were abstracted and a power analysis was done on studies with negative findings. A total of 68 controlled trials met our criteria, 40 of which were published since 1992. Quality scores ranged from 2 to 10, with more recent trials rating higher (mean, 7.7) than earlier studies (mean, 6.4) (P<.001). Effects on physician performance were assessed in 65 studies and 43 found a benefit (66%). These included 9 of 15 studies on drug dosing systems, 1 of 5 studies on diagnostic aids, 14 of 19 preventive care systems, and 19 of 26 studies evaluating CDSSs for other medical care. Six of 14 studies assessing patient outcomes found a benefit. Of the remaining 8 studies, only 3 had a power of greater than 80% to detect a clinically important effect. Published studies of CDSSs are increasing rapidly, and their quality is improving. The CDSSs can enhance clinical performance for drug dosing, preventive care, and other aspects of medical care, but not convincingly for diagnosis. The effects of CDSSs on patient outcomes have been insufficiently studied.

Reliable and robust simulation of individual patients using patient-specific models (PSMs) is one of the next frontiers for modeling and simulation (M&S) in healthcare. PSMs, which form the basis of digital twins, can be employed as clinical tools to, for example, assess disease state, predict response to therapy, or optimize therapy. They may also be used to construct virtual cohorts of patients, for in silico evaluation of medical product safety and/or performance. Methods and frameworks have recently been proposed for evaluating the credibility of M&S in healthcare applications. However, such efforts have generally been motivated by models of medical devices or generic patient models; how best to evaluate the credibility of PSMs has largely been unexplored. The aim of this paper is to understand and demonstrate the credibility assessment process for PSMs using patient-specific cardiac electrophysiological (EP) modeling as an exemplar. We first review approaches used to generate cardiac PSMs and consider how verification, validation, and uncertainty quantification (VVUQ) apply to cardiac PSMs. Next, we execute two simulation studies using a publicly available virtual cohort of 24 patient-specific ventricular models, the first a multi-patient verification study, the second investigating the impact of uncertainty in personalized and non-personalized inputs in a virtual cohort. We then use the findings from our analyses to identify how important characteristics of PSMs can be considered when assessing credibility with the approach of the ASME V&V40 Standard, accounting for PSM concepts such as inter- and intra-user variability, multi-patient and “every-patient” error estimation, uncertainty quantification in personalized vs non-personalized inputs, clinical validation, and others. The results of this paper will be useful to developers of cardiac and other medical image based PSMs, when assessing PSM credibility.

Source Citation Johnston ME, Langton KB, Haynes RB, Mathieu A. Effects of computer-based clinical decision support systems on clinician performance and patient outcome. A critical appraisal of rese...

To review the evidence from controlled trials of the effects of computer-based clinical decision support systems (CDSSs) on clinician performance and patient outcomes. The literature in the MEDLARS, EMBASE, SCISEARCH, and INSPEC databases was searched from 1974 to the present. Conference proceedings and reference lists of relevant articles were reviewed. Evaluators of CDSSs were asked to identify additional studies. 793 citations were examined, and 28 controlled trials that met predefined criteria were reviewed in detail. Study quality was assessed, and data on setting, clinicians and patients, method of allocation, computer system, and outcomes were abstracted and verified using a structured form. Separate summaries were prepared for physician and patient outcomes. Within each of these categories, studies were classified further according to the primary purpose of the CDSS: drug dose determination, diagnosis, or quality assurance. Three of 4 studies of computer-assisted dosing, 1 of 5 studies of computer-aided diagnosis, 4 of 6 studies of preventive care reminder systems, and 7 of 9 studies of computer-aided quality assurance for active medical care that assessed clinician performance showed improvements in clinician performance using a CDSS. Three of 10 studies that assessed patient outcomes reported significant improvements. Strong evidence suggests that some CDSSs can improve physician performance. Additional well-designed studies are needed to assess their effects and cost-effectiveness, especially on patient outcomes.

Evidence-based treatment guidelines embedded in computer-based clinical decision support systems (CCDSS) may improve patient-reported outcomes (PRO). We systematically reviewed the literature for content and application of CCDSS, and their effects on PRO. A systematic review in MEDLINE and EMBASE was conducted according to PRISMA standards. Searches were limited to the publication period 1996-May 2014 and the English language. The search terms covered "computerized clinical decision systems" and "patient-reported outcomes". Screening and extraction was done independently by two reviewers according to predefined inclusion (computer and guideline) and exclusion criteria (no trial, no PRO). Study and CCDSS quality was rated according to predefined criteria. The database searches identified 1,331 references. Eighty-seven full-text articles were analyzed. The main reason for exclusion was no PRO as a study outcome measure. Fifteen studies met the inclusion criteria, representing 13,480 patients. Nine studies used a computerized device to fill in data; in four studies, this was used by the patients themselves. Most of the studies presented the data to the clinician at point of care and incorporated international guidelines. Three studies showed a positive effect on PRO, but only on symptoms. Overall, no negative effects were reported. There was no association with study quality or year of study publication. There are marginal positive effects of CCDSS on specific PRO. Factors that facilitate the use and effect are identified. Easy to use systems with difficult to ignore evidence-based advice need to be developed and tested.

Objective. 4D-CBCT provides phase-resolved images valuable for radiomics analysis for outcome prediction throughout treatment courses. However, 4D-CBCT suffers from streak artifacts caused by under-sampling, which severely degrades the accuracy of radiomic features. Previously we developed group-patient-trained deep learning methods to enhance the 4D-CBCT quality for radiomics analysis, which was not optimized for individual patients. In this study, a patient-specific model was developed to further improve the accuracy of 4D-CBCT based radiomics analysis for individual patients. Approach. This patient-specific model was trained with intra-patient data. Specifically, patient planning 4D-CT was augmented through image translation, rotation, and deformation to generate 305 CT volumes from 10 volumes to simulate possible patient positions during the onboard image acquisition. 72 projections were simulated from 4D-CT for each phase and were used to reconstruct 4D-CBCT using FDK back-projection algorithm. The patient-specific model was trained using these 305 paired sets of patient-specific 4D-CT and 4D-CBCT data to enhance the 4D-CBCT image to match with 4D-CT images as ground truth. For model testing, 4D-CBCT were simulated from a separate set of 4D-CT scan images acquired from the same patient and were then enhanced by this patient-specific model. Radiomics features were then extracted from the testing 4D-CT, 4D-CBCT, and enhanced 4D-CBCT image sets for comparison. The patient-specific model was tested using 4 lung-SBRT patients’ data and compared with the performance of the group-based model. The impact of model dimensionality, region of interest (ROI) selection, and loss function on the model accuracy was also investigated. Main results. Compared with a group-based model, the patient-specific training model further improved the accuracy of radiomic features, especially for features with large errors in the group-based model. For example, the 3D whole-body and ROI loss-based patient-specific model reduces the errors of the first-order median feature by 83.67%, the wavelet LLL feature maximum by 91.98%, and the wavelet HLL skewness feature by 15.0% on average for the four patients tested. In addition, the patient-specific models with different dimensionality (2D versus 3D) or loss functions (L1 versus L1 + VGG + GAN) achieved comparable results for improving the radiomics accuracy. Using whole-body or whole-body+ROI L1 loss for the model achieved better results than using the ROI L1 loss alone as the loss function. Significance. This study demonstrated that the patient-specific model is more effective than the group-based model on improving the accuracy of the 4D-CBCT radiomic features analysis, which could potentially improve the precision for outcome prediction in radiotherapy.

Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known. We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care. We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites. The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines. Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

Computer-based clinical decision support systems (CDSSs) have been championed for their potential to improve healthcare quality. However, there has been no systematic study of the types of CDSSs that have been developed. In previous work, we developed the CDSS Taxonomy for comprehensively describing the technical, workflow, and contextual characteristics of CDSSs. We now use the CDSS Taxonomy to describe outpatient CDSSs evaluated in randomized controlled trials published between 1998 and 2002. 31 studies comprising 42 CDSS systems were included in our analysis. The majority of systems used rule-based reasoning engines to &amp;ldquo;push&amp;rdquo; explicit, individualized recommendations concerning non-urgent decisions to clinicians or patients, but not both. 71% of the systems required someone to manually enter data into the system or to process the system output for use by the target decision maker. The average kappa for coding agreement was &gt; 0.6. Our findings demonstrate that outpatient CDSSs vary greatly in design and function. Many impose a data entry or output processing burden on clinic staff. More complete reporting of CDSS characteristics is needed in the literature.

Computer-based clinical decision support systems (CDSSs) have been promoted for their potential to improve quality of health care. However, given the limited range of clinical settings in which they have been tested, such systems must be evaluated rigorously before widespread introduction into clinical practice. To determine whether presentation of venous thromboembolism prophylaxis guidelines using a CDSS increases the proportion of appropriate clinical practice decisions made. Time-series study conducted between December 1997 and July 1999. Orthopedic surgery department of a teaching hospital in Paris, France. A total of 1971 patients who underwent orthopedic surgery. A CDSS designed to provide immediate information pertaining to venous thromboembolism prevention among surgical patients was integrated into daily medical practice during three 10-week intervention periods, alternated with four 10-week control periods, with a 4-week washout between each period. Proportion of appropriate prescriptions ordered for anticoagulation, according to preestablished clinical guidelines, during intervention vs control periods. Physicians complied with guidelines in 82.8% (95% confidence interval [CI], 77.6%-87.1%) of cases during control periods and in 94.9% (95% CI, 92.5%-96.6%) of cases during intervention periods. During each intervention period, the appropriateness of prescription increased significantly (P<.001). Each time the CDSS was removed, physician practice reverted to that observed before initiation of the intervention. The relative risk of inappropriate practice decisions during control periods vs intervention periods was 3.8 (95% CI, 2.7-5.4). In our study, implementation of clinical guidelines for venous thromboembolism prophylaxis through a CDSS used routinely in an orthopedic surgery department and integrated into the hospital information system changed physician behavior and improved compliance with guidelines. JAMA. 2000;283:2816-2821

Objective: A literature review was conducted of studies investigating the effectiveness of paper- and computer-based clinical decision support systems (CDSS) used with or without educational programs designed to increase the use of venous thromboembolism (VTE) prophylaxis. Methods: Medline was searched on August 9, 2010, without limits on publication year, but with restrictions to English-language articles only. The search terms used were “venous thromboembolism,” “deep vein thrombosis,” “pulmonary embolism,” “prophylaxis,” “thromboprophylaxis,” “computerized,” “computerised,” “decision support,” “alerts,” “reminder,” “paper system,” “risk assessment,” and “risk score.” All types of studies regarding the effects of CDSS on VTE prophylaxis rates were included. Studies were included if ≥ 1 post-implementation outcome was measured, such as rates of VTE, rates of prophylaxis prescribing, or guideline-adherence measures. Results: Studies evaluating paper-based CDSS used different strategies, including risk-assessment forms with prophylaxis recommendations, standard order sets, and preprinted sticker reminders on patient notes. Paper-based systems consistently improved prophylaxis rates; however, in most studies, there was still room for improvement. Furthermore, the effect of paper-based CDSS on VTE rates was not conclusively established. Studies evaluating computer-based systems used approaches including risk-assessment models integrated in the computerized physician order entry system, with or without alerts, and automatic reminders on operating schedules. Conclusion: Computerized systems are associated with substantial improvements in the prescribing of appropriate prophylaxis and reductions in VTE events, particularly in medical patients. More robust systems can be established with computer-based rather than paper-based CDSS. A drawback of computerized systems is that some hospitals may not have adequate information technology system resources.

Even with an annual expenditure of more than $3 trillion, the U.S. healthcare system is far from optimal. For example, the third leading cause of death in the U.S. is preventable medical error, immediately after heart disease and cancer. Computer-based clinical decision support systems (CDSSs) have been proposed to address such deficiencies and have significantly improved clinical practice over the past decade. However, they remain limited to clinics and hospitals, and do not take advantage of patient data that are obtained on a daily basis using wearable medical sensors (WMSs) that have the ability to bridge this information gap. WMSs can collect physiological signals from anyone anywhere anytime. Thus, they have the potential to usher in an era of pervasive healthcare. However, most prior work on WMSs only focuses on hardware and protocol design, and not on an information system that can fully utilize the collected signals for efficient disease diagnosis. In this paper, for the first time, we introduce a hierarchical health decision support system for disease diagnosis that integrates health data from WMSs into CDSSs. The proposed system has a multi-tier structure, starting with a WMS tier, backed by robust machine learning, that enables diseases to be tracked individually by a disease diagnosis module. We demonstrate the feasibility of such a system through six disease diagnosis modules aimed at four ICD-10-CM disease categories. We show that the system is scalable using five more disease categories. Just the WMS tier offers impressive diagnostic accuracies for various diseases: arrhythmia (86 percent), type-2 diabetes (78 percent), urinary bladder disorder (99 percent), renal pelvis nephritis (94 percent), and hypothyroid (95 percent). We estimate that the disease diagnosis modules of all known 69,000 human diseases would require just 62 GB of storage space in the WMS tier. This is practical even in today’s cloud or base station oriented WMS systems.

Phenotype- and patient-specific modelling in asthma: Bronchial thermoplasty and uncertainty quantification